69 research outputs found
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning
Large Language Models (LLMs) prompted to generate chain-of-thought (CoT)
exhibit impressive reasoning capabilities. Recent attempts at prompt
decomposition toward solving complex, multi-step reasoning problems depend on
the ability of the LLM to simultaneously decompose and solve the problem. A
significant disadvantage is that foundational LLMs are typically not available
for fine-tuning, making adaptation computationally prohibitive. We believe (and
demonstrate) that problem decomposition and solution generation are distinct
capabilites, better addressed in separate modules, than by one monolithic LLM.
We introduce DaSLaM, which uses a decomposition generator to decompose complex
problems into subproblems that require fewer reasoning steps. These subproblems
are answered by a solver. We use a relatively small (13B parameters) LM as the
decomposition generator, which we train using policy gradient optimization to
interact with a solver LM (regarded as black-box) and guide it through
subproblems, thereby rendering our method solver-agnostic. Evaluation on
multiple different reasoning datasets reveal that with our method, a 175
billion parameter LM (text-davinci-003) can produce competitive or even better
performance, compared to its orders-of-magnitude larger successor, GPT-4.
Additionally, we show that DaSLaM is not limited by the solver's capabilities
as a function of scale; e.g., solver LMs with diverse sizes give significant
performance improvement with our solver-agnostic decomposition technique.
Exhaustive ablation studies evince the superiority of our modular finetuning
technique over exorbitantly large decomposer LLMs, based on prompting alone.Comment: EMNLP 202
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach
HTTP based adaptive video streaming has become a popular choice of streaming
due to the reliable transmission and the flexibility offered to adapt to
varying network conditions. However, due to rate adaptation in adaptive
streaming, the quality of the videos at the client keeps varying with time
depending on the end-to-end network conditions. Further, varying network
conditions can lead to the video client running out of playback content
resulting in rebuffering events. These factors affect the user satisfaction and
cause degradation of the user quality of experience (QoE). It is important to
quantify the perceptual QoE of the streaming video users and monitor the same
in a continuous manner so that the QoE degradation can be minimized. However,
the continuous evaluation of QoE is challenging as it is determined by complex
dynamic interactions among the QoE influencing factors. Towards this end, we
present LSTM-QoE, a recurrent neural network based QoE prediction model using a
Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded
LSTM blocks to capture the nonlinearities and the complex temporal dependencies
involved in the time varying QoE. Based on an evaluation over several publicly
available continuous QoE databases, we demonstrate that the LSTM-QoE has the
capability to model the QoE dynamics effectively. We compare the proposed model
with the state-of-the-art QoE prediction models and show that it provides
superior performance across these databases. Further, we discuss the state
space perspective for the LSTM-QoE and show the efficacy of the state space
modeling approaches for QoE prediction
Modeling Continuous Video QoE Evolution: A State Space Approach
A rapid increase in the video traffic together with an increasing demand for
higher quality videos has put a significant load on content delivery networks
in the recent years. Due to the relatively limited delivery infrastructure, the
video users in HTTP streaming often encounter dynamically varying quality over
time due to rate adaptation, while the delays in video packet arrivals result
in rebuffering events. The user quality-of-experience (QoE) degrades and varies
with time because of these factors. Thus, it is imperative to monitor the QoE
continuously in order to minimize these degradations and deliver an optimized
QoE to the users. Towards this end, we propose a nonlinear state space model
for efficiently and effectively predicting the user QoE on a continuous time
basis. The QoE prediction using the proposed approach relies on a state space
that is defined by a set of carefully chosen time varying QoE determining
features. An evaluation of the proposed approach conducted on two publicly
available continuous QoE databases shows a superior QoE prediction performance
over the state-of-the-art QoE modeling approaches. The evaluation results also
demonstrate the efficacy of the selected features and the model order employed
for predicting the QoE. Finally, we show that the proposed model is completely
state controllable and observable, so that the potential of state space
modeling approaches can be exploited for further improving QoE prediction.Comment: 7 pages, 3 figures, conferenc
A novel mitragynine analog with low-efficacy mu opioid receptor agonism displays antinociception with attenuated adverse effects
Mitragynine and 7-hydroxymitragynine (7OH) are the major alkaloids mediating the biological actions of the psychoactive plant kratom. To investigate the structure-activity relationships of mitragynine/7OH templates, we diversified the aromatic ring of the indole at the C9, C10, and C12 positions and investigated their G-protein and arrestin signaling mediated by mu opioid receptors (MOR). Three synthesized lead C9 analogs replacing the 9-OC
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